Modeling of Nitrate Adsorption on Granular Activated Carbon (GAC) using Artificial Neural Network (ANN)

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
Alireza Khataee ◽  
Ali Khani
Keyword(s):  
Neural Network ◽  
Activated Carbon ◽  
Adsorption Process ◽  
Granular Activated Carbon ◽  
Spectrophotometric Analysis ◽  
Ann Model ◽  
Effective Parameters ◽  
Neuron Network ◽  
Feed Forward Back Propagation ◽  
Nitrate Adsorption

High concentrations of N-containing compounds in drinking water cause health problems such as cyanosis among children and cancer of the alimentary canal. Therefore, removal of nitrate from water samples is of significant importance from the health and environmental point of view. In this work, the effective parameters on removal of nitrate by adsorption process, which included the amount of granular activated carbon (m), initial concentration (C0), contact time, pH and temperature (T), were investigated. The removal process was monitored using an on-line spectrophotometric analysis system. It was found that the content of adsorption followed decreasing order: m= 10>5>2>1g, C0= 20>15>25>10 ppm, pH=4>7>10>1 and T=25>35>45>55 0C. The three-layered feed forward back propagation neural network was used for modeling of nitrate adsorption on granular activated carbon. The comparison between the predicted results of the designed ANN model and the experimental data proved that modeling of nitrate adsorption process using artificial neuron network was a good and precise method to predict the extent of adsorption of nitrate on GAC under different conditions.

scholarly journals Adsorption of Nitrate onto ZnCl2-Modified Coconut Granular Activated Carbon: Kinetics, Characteristics, and Adsorption Dynamics

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Lingjie Liu ◽  
Min Ji ◽  
Fen Wang
Keyword(s):  
Activated Carbon ◽  
Granular Activated Carbon ◽  
Kinetic Studies ◽  
Surface Characteristics ◽  
Electrostatic Attraction ◽  
Batch Adsorption ◽  
Dynamic Adsorption ◽  
Batch Experiments ◽  
Nitrate Adsorption ◽  
Pseudo Second Order

Coconut granular activated carbon (CGAC) was modified by impregnating with ZnCl2solution to remove nitrate from aqueous solutions. Sorption isotherm and kinetic studies were carried out in a series of batch experiments. Nitrate adsorption of both ZnCl2-modified CGAC and CGAC fitted the Langmuir and Freundlich models. Batch adsorption isotherms indicated that the maximum adsorption capacities of ZnCl2-modified CGAC and CGAC were calculated as 14.01 mgN·g−1and 0.28 mgN·g−1, respectively. The kinetic data obtained from batch experiments were well described by pseudo-second-order model. The column study was used to analyze the dynamic adsorption process. The highest bed adsorption capacity of 1.76 mgN·g−1was obtained by 50 mgN·L−1inlet nitrate concentration, 20 g adsorbents, and 10 ml·min−1flow rate. The dynamic adsorption data were fitted well to the Thomas and Yoon–Nelson models with coefficients of correlationR2 > 0.834 at different conditions. Surface characteristics and pore structures of CGAC and ZnCl2-modified CGAC were performed by SEM and EDAX and BET and indicated that ZnCl2had adhered to the surface of GAC after modified. Zeta potential, Raman spectra, and FTIR suggested the electrostatic attraction between the nitrate ions and positive charge. The results revealed that the mechanism of adsorption nitrate mainly depended on electrostatic attraction almost without any chemical interactions.

scholarly journals Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5773-5779
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Food Waste ◽  
Back Propagation ◽  
Ann Model ◽  
Neuron Network ◽  
Volatile Organic ◽  
Artificial Neural

This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.

Application of Back Propagation Neural Network Algorithms on Modeling Failure of B-737 Bleed Air System Valves in Desert Conditions

2012 ◽  
Vol 225 ◽  
pp. 505-510 ◽  
Author(s):  
Wael G. Abdelrahman ◽  
Ahmed Z. Al-Garni ◽  
Waheed Al-Wadiee
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Aircraft Engine ◽  
Weibull Model ◽  
Back Propagation Neural Network ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Failure Data ◽  
Feed Forward Back Propagation ◽  
Air System

Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators’ profits. The engine bleed air system valves have considerably high failure rates when the engines are operated in desert conditions because of sand particles erosion and blockage. In this work, an Artificial Neural Network (ANN) model for the prediction of failure rate of the most important of these valves in Boeing 737 engines is developed and validated. A previously developed feed-forward back-propagation algorithm is implemented to train the ANN. The effects of changing the number of neurons in the input layer, the number of neurons in the hidden layer, the rate of learning, and the momentum constant are investigated. The model results are validated using comparisons with actual valves failure data from a local operator in Saudi Arabia, as well as comparisons with classical Weibull model results.

scholarly journals Prediction of the thermo-physical properties of deep-fat frying plantain chips (ipekere) using artificial neural network

2021 ◽  
Vol 25 (2) ◽  
pp. 253-260
Author(s):  
James Abiodun Adeyanju ◽  
John Oluranti Olajide ◽  
Emmanuel Olusola Oke ◽  
Jelili Babatunde Hussein ◽  
Chiamaka Jane Ude
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Properties ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Deep Fat Frying ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Thermo Physical Properties

Abstract This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m3 while thermal diffusivity and conductivity were least affected with 0.192 x 10−6 m2/s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips.

scholarly journals Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

2017 ◽  
Vol 3 (2) ◽  
pp. 78-87 ◽  
Author(s):  
Ajaykumar Bhagubhai Patel ◽  
Geeta S. Joshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Basin ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Feed Forward Back Propagation ◽  
Sabarmati River ◽  
Artificial Neural

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

Forecasting Coconut Yield: A Comparative Study between the Use of Traditional Forecasting and Feed Forward Back Propagation Artificial Neural Network

10.17158/320 ◽  
2014 ◽  
Vol 18 (2) ◽  
Author(s):  
Eric John G. Emberda ◽  
Den Ryan L. Dumas ◽  
Timothy Pierce M. Rentillo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Forecast Data ◽  
The Mean ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>This study compared the use of Linear Regression and Feed Forward Backpropagation Artificial Neural Network (ANN) in forecasting the coconut yield and copra yield of a selected area in Davao region. Raw data were gathered from the Philippine Coconut Authority, Davao Research Center. An ANN model was created and tested repeatedly to the best combination of nodes. Accuracy of the forecast between the two methods was compared by looking at the mean square error and the standard error for variable x and y. Results showed that the use of Feed Forward Back Propagation Artificial Neural Network gives better accuracy of the forecast data.</p>

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